Healthcare Tech and Its Help In Diagnosing Patients

By Brooke Faulkner, freelance writer; @faulknercreek.

Up to a fifth of patients with serious conditions are first misdiagnosed, and that leaves tremendous consequences. With the help of healthcare technology, doctors are able to diagnosis patients more effectively and easier. For example, migrating patient data from paper to online, known as electronic health records (EHRs), has greatly aided the medical world. Technology, especially using artificial intelligence and predictive analytics, has enabled doctors to make faster, more accurate diagnoses, and thus provide better care.

The volume of big data

Duquesne University estimated there to be 150 exabytes of healthcare data collected in 2011. Four years later, they reported about 83 percent of doctors had transitioned from using paper to electronic records. By now, with the ubiquity of the cloud, these numbers have assuredly gone up.

Massive amounts of data make predictive analytics possible, as trends can be spotted and analyzed. By spotting patterns, diagnosis of a disease becomes easier even for doctors unfamiliar with a specific disease or symptom. Uploading symptoms allows a computer to compare records and identify symptoms comorbid of other problems. This allows even specialized doctors to recognize issues outside of their field. Medical mistakes lead to the death of some 440,000 people each year; while misdiagnosis is only a part of this number, correct diagnosis and treatment will reduce it.

Big data can even be collected in the form of PDFs as part of telemedicine. A doctor can send PDFs to patients as part of a poll or survey or simply to collect symptom information from the patient. From there, data entered in the PDF can be collected and analyzed, generating patient data or feedback for the doctor.

Google flu trends

Google ran what can best be called an experiment from 2008 to 2014. Using artificial intelligence, the search engine recorded flu-related searches in an attempt to predict the severity of an outbreak, as well as the affected geographical area.

It was a flawed model, and tried to use big data as a replacement, rather than a supplement, for traditional data collection and analysis. It completely missed a flu outbreak in 2013, the data off by a massive 140 percent, and Google Flu Trends ended its public version in 2014. The algorithm monitoring flu-related search terms was simply not sophisticated enough to provide accurate results. While new data is no longer available to the public, historical data remains available to the Centers for Disease Control and other research groups. It’s possible that once the algorithm and predictive analysis is capable, the program will continue.

Wearables and IoT

Wearables, internet of things (IoT) devices like a FitBit, are capable of tracking biometric data, including heart rate and sleep patterns. This data, in turn, can be sent to a patient’s doctor or collected in apps. Again, using predictive analysis, an algorithm can scour the data and detect signs or symptoms indicative of something abnormal, alerting the patient and their doctor.

IoT-collected data can also be used as part of a more holistic diagnosis, combined with other data the doctor has collected, such as at a physical appointment.

Entrepreneur and app developer Juan-Pablo Segura offered his take, targeting pregnant women, on this idea on The Pitch podcast. He described to potential investors that his app connects to IoT blood pressure cuffs and a weight scale. Each is used once a week, and the app sends data to the woman’s OB-GYN. This reduces the number of physical appointments needed and improves the amount of data the doctor has to work with. This can greatly aid in improving rural healthcare as well, as the woman may not have easy access to her doctor, but the doctor has easy access to the data, allowing for an easier diagnosis of unexpected problems.

Norwich University found that 91 percent of hospitals seek to improve their operational efficiency, while 89 percent listed improving patient and caretaker safety as a driving factor of change. Using technology to aid doctors in diagnosing problems can help both. Patients will receive better care, both in terms of quality and efficiency, while hospitals and doctors will be better at their jobs. As artificial intelligence and predictive analysis improve, so too will patient care, giving a win-win scenario to healthcare providers and patients.

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